Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations95798
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.2 MiB
Average record size in memory112.0 B

Variable types

Numeric10
Categorical3

Alerts

Area_in_hectares is highly overall correlated with Production_in_tonsHigh correlation
Crop is highly overall correlated with Crop_Type and 4 other fieldsHigh correlation
Crop_Type is highly overall correlated with Crop and 1 other fieldsHigh correlation
K is highly overall correlated with Crop and 2 other fieldsHigh correlation
N is highly overall correlated with Crop and 2 other fieldsHigh correlation
P is highly overall correlated with CropHigh correlation
Production_in_tons is highly overall correlated with Area_in_hectaresHigh correlation
State_Name is highly overall correlated with rainfall and 1 other fieldsHigh correlation
Yield_ton_per_hec is highly overall correlated with K and 1 other fieldsHigh correlation
pH is highly overall correlated with CropHigh correlation
rainfall is highly overall correlated with Crop_Type and 1 other fieldsHigh correlation
temperature is highly overall correlated with State_NameHigh correlation
Unnamed: 0 has unique valuesUnique

Reproduction

Analysis started2025-11-21 05:46:27.145758
Analysis finished2025-11-21 05:46:40.536021
Duration13.39 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

Unique 

Distinct95798
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49883.041
Minimum0
Maximum99848
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-21T11:16:40.618500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4882.85
Q124470.25
median50360
Q374913.75
95-th percentile94858.15
Maximum99848
Range99848
Interquartile range (IQR)50443.5

Descriptive statistics

Standard deviation28952.226
Coefficient of variation (CV)0.58040218
Kurtosis-1.2145184
Mean49883.041
Median Absolute Deviation (MAD)25214.5
Skewness-0.0063238083
Sum4.7786956 × 109
Variance8.3823139 × 108
MonotonicityStrictly increasing
2025-11-21T11:16:40.733357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
998481
 
< 0.1%
01
 
< 0.1%
11
 
< 0.1%
21
 
< 0.1%
31
 
< 0.1%
41
 
< 0.1%
998321
 
< 0.1%
998311
 
< 0.1%
998291
 
< 0.1%
998281
 
< 0.1%
Other values (95788)95788
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
998481
< 0.1%
998471
< 0.1%
998461
< 0.1%
998451
< 0.1%
998441
< 0.1%
998431
< 0.1%
998421
< 0.1%
998411
< 0.1%
998401
< 0.1%
998391
< 0.1%

State_Name
Categorical

High correlation 

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
uttar pradesh
12536 
karnataka
8910 
madhya pradesh
8702 
bihar
8437 
odisha
6236 
Other values (28)
50977 

Length

Max length27
Median length16
Mean length9.7809662
Min length3

Characters and Unicode

Total characters936997
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowandhra pradesh
2nd rowandhra pradesh
3rd rowandhra pradesh
4th rowandhra pradesh
5th rowandhra pradesh

Common Values

ValueCountFrequency (%)
uttar pradesh12536
13.1%
karnataka8910
 
9.3%
madhya pradesh8702
 
9.1%
bihar8437
 
8.8%
odisha6236
 
6.5%
tamil nadu5566
 
5.8%
assam5495
 
5.7%
rajasthan5358
 
5.6%
maharashtra4162
 
4.3%
west bengal3563
 
3.7%
Other values (23)26833
28.0%

Length

2025-11-21T11:16:40.836292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pradesh26974
20.1%
uttar12536
 
9.4%
karnataka8910
 
6.6%
madhya8702
 
6.5%
bihar8437
 
6.3%
odisha6236
 
4.7%
tamil5566
 
4.2%
nadu5566
 
4.2%
assam5495
 
4.1%
rajasthan5358
 
4.0%
Other values (32)40286
30.0%

Most occurring characters

ValueCountFrequency (%)
a226498
24.2%
r90749
9.7%
h88792
 
9.5%
t67866
 
7.2%
s61805
 
6.6%
d56589
 
6.0%
n42385
 
4.5%
e40166
 
4.3%
38268
 
4.1%
m30913
 
3.3%
Other values (14)192966
20.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)936997
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a226498
24.2%
r90749
9.7%
h88792
 
9.5%
t67866
 
7.2%
s61805
 
6.6%
d56589
 
6.0%
n42385
 
4.5%
e40166
 
4.3%
38268
 
4.1%
m30913
 
3.3%
Other values (14)192966
20.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)936997
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a226498
24.2%
r90749
9.7%
h88792
 
9.5%
t67866
 
7.2%
s61805
 
6.6%
d56589
 
6.0%
n42385
 
4.5%
e40166
 
4.3%
38268
 
4.1%
m30913
 
3.3%
Other values (14)192966
20.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)936997
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a226498
24.2%
r90749
9.7%
h88792
 
9.5%
t67866
 
7.2%
s61805
 
6.6%
d56589
 
6.0%
n42385
 
4.5%
e40166
 
4.3%
38268
 
4.1%
m30913
 
3.3%
Other values (14)192966
20.6%

Crop_Type
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
kharif
37785 
rabi
26878 
whole year
24271 
summer
6864 

Length

Max length10
Median length6
Mean length6.452285
Min length4

Characters and Unicode

Total characters618116
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowkharif
2nd rowkharif
3rd rowkharif
4th rowkharif
5th rowkharif

Common Values

ValueCountFrequency (%)
kharif37785
39.4%
rabi26878
28.1%
whole year24271
25.3%
summer6864
 
7.2%

Length

2025-11-21T11:16:40.917912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-21T11:16:40.977207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
kharif37785
31.5%
rabi26878
22.4%
whole24271
20.2%
year24271
20.2%
summer6864
 
5.7%

Most occurring characters

ValueCountFrequency (%)
r95798
15.5%
a88934
14.4%
i64663
10.5%
h62056
10.0%
e55406
9.0%
k37785
 
6.1%
f37785
 
6.1%
b26878
 
4.3%
w24271
 
3.9%
o24271
 
3.9%
Other values (6)100269
16.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)618116
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r95798
15.5%
a88934
14.4%
i64663
10.5%
h62056
10.0%
e55406
9.0%
k37785
 
6.1%
f37785
 
6.1%
b26878
 
4.3%
w24271
 
3.9%
o24271
 
3.9%
Other values (6)100269
16.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)618116
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r95798
15.5%
a88934
14.4%
i64663
10.5%
h62056
10.0%
e55406
9.0%
k37785
 
6.1%
f37785
 
6.1%
b26878
 
4.3%
w24271
 
3.9%
o24271
 
3.9%
Other values (6)100269
16.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)618116
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r95798
15.5%
a88934
14.4%
i64663
10.5%
h62056
10.0%
e55406
9.0%
k37785
 
6.1%
f37785
 
6.1%
b26878
 
4.3%
w24271
 
3.9%
o24271
 
3.9%
Other values (6)100269
16.2%

Crop
Categorical

High correlation 

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
rice
11295 
maize
9368 
moong
6704 
wheat
6177 
sesamum
6081 
Other values (30)
56173 

Length

Max length11
Median length10
Mean length6.1367565
Min length4

Characters and Unicode

Total characters587889
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcotton
2nd rowhorsegram
3rd rowjowar
4th rowmaize
5th rowmoong

Common Values

ValueCountFrequency (%)
rice11295
 
11.8%
maize9368
 
9.8%
moong6704
 
7.0%
wheat6177
 
6.4%
sesamum6081
 
6.3%
rapeseed5342
 
5.6%
potato5297
 
5.5%
jowar5118
 
5.3%
onion4930
 
5.1%
sunflower3631
 
3.8%
Other values (25)31855
33.3%

Length

2025-11-21T11:16:41.064475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rice11295
 
11.8%
maize9368
 
9.8%
moong6704
 
7.0%
wheat6177
 
6.4%
sesamum6081
 
6.3%
rapeseed5342
 
5.6%
potato5297
 
5.5%
jowar5118
 
5.3%
onion4930
 
5.1%
sunflower3631
 
3.8%
Other values (25)31855
33.3%

Most occurring characters

ValueCountFrequency (%)
e75433
12.8%
a72493
12.3%
o63516
10.8%
r50402
 
8.6%
t38208
 
6.5%
i36791
 
6.3%
n34753
 
5.9%
m34276
 
5.8%
s30455
 
5.2%
c24666
 
4.2%
Other values (13)126896
21.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)587889
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e75433
12.8%
a72493
12.3%
o63516
10.8%
r50402
 
8.6%
t38208
 
6.5%
i36791
 
6.3%
n34753
 
5.9%
m34276
 
5.8%
s30455
 
5.2%
c24666
 
4.2%
Other values (13)126896
21.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)587889
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e75433
12.8%
a72493
12.3%
o63516
10.8%
r50402
 
8.6%
t38208
 
6.5%
i36791
 
6.3%
n34753
 
5.9%
m34276
 
5.8%
s30455
 
5.2%
c24666
 
4.2%
Other values (13)126896
21.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)587889
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e75433
12.8%
a72493
12.3%
o63516
10.8%
r50402
 
8.6%
t38208
 
6.5%
i36791
 
6.3%
n34753
 
5.9%
m34276
 
5.8%
s30455
 
5.2%
c24666
 
4.2%
Other values (13)126896
21.6%

N
Real number (ℝ)

High correlation 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.848744
Minimum10
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-21T11:16:41.141935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile20
Q150
median70
Q380
95-th percentile180
Maximum180
Range170
Interquartile range (IQR)30

Descriptive statistics

Standard deviation39.815456
Coefficient of variation (CV)0.57002393
Kurtosis1.0337132
Mean69.848744
Median Absolute Deviation (MAD)20
Skewness0.93193874
Sum6691370
Variance1585.2705
MonotonicityNot monotonic
2025-11-21T11:16:41.213625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
8027074
28.3%
5015360
16.0%
2012098
12.6%
1207937
 
8.3%
606177
 
6.4%
306081
 
6.3%
1805297
 
5.5%
1004157
 
4.3%
703786
 
4.0%
902878
 
3.0%
Other values (4)4953
 
5.2%
ValueCountFrequency (%)
102108
 
2.2%
2012098
12.6%
252535
 
2.6%
306081
 
6.3%
5015360
16.0%
606177
 
6.4%
703786
 
4.0%
75203
 
0.2%
8027074
28.3%
902878
 
3.0%
ValueCountFrequency (%)
1805297
 
5.5%
160107
 
0.1%
1207937
 
8.3%
1004157
 
4.3%
902878
 
3.0%
8027074
28.3%
75203
 
0.2%
703786
 
4.0%
606177
 
6.4%
5015360
16.0%

P
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.647947
Minimum10
Maximum125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-21T11:16:41.282746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile15
Q140
median40
Q360
95-th percentile60
Maximum125
Range115
Interquartile range (IQR)20

Descriptive statistics

Standard deviation14.849365
Coefficient of variation (CV)0.35654496
Kurtosis0.60088237
Mean41.647947
Median Absolute Deviation (MAD)0
Skewness0.094755041
Sum3989790
Variance220.50366
MonotonicityNot monotonic
2025-11-21T11:16:41.350827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4049916
52.1%
6021898
22.9%
156249
 
6.5%
306177
 
6.4%
205224
 
5.5%
752551
 
2.7%
102302
 
2.4%
501355
 
1.4%
12591
 
0.1%
6535
 
< 0.1%
ValueCountFrequency (%)
102302
 
2.4%
156249
 
6.5%
205224
 
5.5%
306177
 
6.4%
4049916
52.1%
501355
 
1.4%
6021898
22.9%
6535
 
< 0.1%
752551
 
2.7%
12591
 
0.1%
ValueCountFrequency (%)
12591
 
0.1%
752551
 
2.7%
6535
 
< 0.1%
6021898
22.9%
501355
 
1.4%
4049916
52.1%
306177
 
6.4%
205224
 
5.5%
156249
 
6.5%
102302
 
2.4%

K
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.967003
Minimum10
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-21T11:16:41.418698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile20
Q120
median30
Q350
95-th percentile100
Maximum200
Range190
Interquartile range (IQR)30

Descriptive statistics

Standard deviation28.312965
Coefficient of variation (CV)0.67464824
Kurtosis2.8072415
Mean41.967003
Median Absolute Deviation (MAD)10
Skewness1.7391263
Sum4020355
Variance801.62398
MonotonicityNot monotonic
2025-11-21T11:16:41.496681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2034439
35.9%
4017706
18.5%
3016127
16.8%
905491
 
5.7%
654930
 
5.1%
504263
 
4.4%
453083
 
3.2%
1202985
 
3.1%
602770
 
2.9%
1002535
 
2.6%
Other values (5)1469
 
1.5%
ValueCountFrequency (%)
10120
 
0.1%
2034439
35.9%
3016127
16.8%
4017706
18.5%
453083
 
3.2%
504263
 
4.4%
602770
 
2.9%
654930
 
5.1%
7035
 
< 0.1%
905491
 
5.7%
ValueCountFrequency (%)
20091
 
0.1%
150203
 
0.2%
1401020
 
1.1%
1202985
3.1%
1002535
2.6%
905491
5.7%
7035
 
< 0.1%
654930
5.1%
602770
2.9%
504263
4.4%

pH
Real number (ℝ)

High correlation 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6367209
Minimum3.82
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-21T11:16:42.013272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3.82
5-th percentile4.92
Q15.36
median5.54
Q35.92
95-th percentile6.6
Maximum7
Range3.18
Interquartile range (IQR)0.56

Descriptive statistics

Standard deviation0.50210457
Coefficient of variation (CV)0.089077422
Kurtosis-0.02829505
Mean5.6367209
Median Absolute Deviation (MAD)0.2
Skewness0.60625494
Sum539986.59
Variance0.252109
MonotonicityNot monotonic
2025-11-21T11:16:42.137578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.362826
 
2.9%
5.422781
 
2.9%
5.42769
 
2.9%
5.382760
 
2.9%
5.322749
 
2.9%
5.62748
 
2.9%
5.622715
 
2.8%
5.682709
 
2.8%
5.542708
 
2.8%
5.52708
 
2.8%
Other values (91)68325
71.3%
ValueCountFrequency (%)
3.8211
< 0.1%
3.846
< 0.1%
3.8611
< 0.1%
3.8810
< 0.1%
3.912
< 0.1%
3.9214
< 0.1%
3.9413
< 0.1%
3.9610
< 0.1%
3.9811
< 0.1%
47
< 0.1%
ValueCountFrequency (%)
7548
0.6%
6.9548
0.6%
6.8578
0.6%
6.7540
0.6%
6.68576
0.6%
6.66588
0.6%
6.64604
0.6%
6.62551
0.6%
6.61124
1.2%
6.58587
0.6%

rainfall
Real number (ℝ)

High correlation 

Distinct111
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean697.17581
Minimum3.274569
Maximum3322.06
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-21T11:16:42.254303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3.274569
5-th percentile41.3
Q1157.31
median579.75
Q31110.78
95-th percentile1712.66
Maximum3322.06
Range3318.7854
Interquartile range (IQR)953.47

Descriptive statistics

Standard deviation604.18354
Coefficient of variation (CV)0.86661575
Kurtosis1.475034
Mean697.17581
Median Absolute Deviation (MAD)446.89
Skewness1.1354534
Sum66788049
Variance365037.75
MonotonicityNot monotonic
2025-11-21T11:16:42.369059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
579.754871
 
5.1%
75.324794
 
5.0%
1011.493485
 
3.6%
884.53451
 
3.6%
1111.683423
 
3.6%
1246.7153106
 
3.2%
840.462717
 
2.8%
87.22584
 
2.7%
510.052550
 
2.7%
607.482503
 
2.6%
Other values (101)62314
65.0%
ValueCountFrequency (%)
3.27456945
 
< 0.1%
3.94106
 
0.1%
5.27431
 
< 0.1%
9.62704416
 
< 0.1%
10.26574870
 
0.1%
15.34594
 
0.6%
19.381360
1.4%
34.811677
1.8%
35.21423
 
< 0.1%
37.09234
 
0.2%
ValueCountFrequency (%)
3322.0673
 
0.1%
3041.418
 
< 0.1%
2879.8629
 
< 0.1%
2817.861386
1.4%
2569.52272
 
0.3%
2459.648
 
< 0.1%
2169.322399
2.5%
1997.12339
 
0.4%
1925.6820
 
< 0.1%
1875.6136
 
0.1%

temperature
Real number (ℝ)

High correlation 

Distinct109
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.639702
Minimum1.18
Maximum35.346667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-21T11:16:42.489497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.18
5-th percentile20.1
Q122.890909
median27.276
Q329.266667
95-th percentile34.01
Maximum35.346667
Range34.166667
Interquartile range (IQR)6.3757576

Descriptive statistics

Standard deviation4.8947147
Coefficient of variation (CV)0.18373759
Kurtosis2.2810095
Mean26.639702
Median Absolute Deviation (MAD)3.3406667
Skewness-0.76221439
Sum2552030.2
Variance23.958232
MonotonicityNot monotonic
2025-11-21T11:16:42.615412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.014871
 
5.1%
22.6764794
 
5.0%
30.433485
 
3.6%
27.654545453451
 
3.6%
28.648181823423
 
3.6%
22.63106
 
3.2%
33.583333332717
 
2.8%
23.1062584
 
2.7%
33.373333332550
 
2.7%
26.366666672503
 
2.6%
Other values (99)62314
65.0%
ValueCountFrequency (%)
1.18170
 
0.2%
4.9272
0.3%
10.38544
0.6%
11.2464
0.5%
12.5137
 
0.1%
14.6582
0.6%
14.7326
0.3%
15.5162
 
0.2%
15.618181828
 
< 0.1%
15.852246
0.3%
ValueCountFrequency (%)
35.34666667730
 
0.8%
34.923333331165
 
1.2%
34.73576
 
0.6%
34.666666671677
 
1.8%
34.014871
5.1%
33.76333333106
 
0.1%
33.583333332717
2.8%
33.373333332550
2.7%
30.616666671300
 
1.4%
30.433485
3.6%

Area_in_hectares
Real number (ℝ)

High correlation 

Distinct25977
Distinct (%)27.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16772.773
Minimum0.58
Maximum726300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-21T11:16:42.731760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.58
5-th percentile10
Q1140
median1087
Q38500
95-th percentile100626.9
Maximum726300
Range726299.42
Interquartile range (IQR)8360

Descriptive statistics

Standard deviation43856.481
Coefficient of variation (CV)2.6147424
Kurtosis31.42573
Mean16772.773
Median Absolute Deviation (MAD)1065
Skewness4.7182201
Sum1.6067981 × 109
Variance1.9233909 × 109
MonotonicityNot monotonic
2025-11-21T11:16:42.846924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5568
 
0.6%
2562
 
0.6%
3528
 
0.6%
4521
 
0.5%
1495
 
0.5%
10492
 
0.5%
6465
 
0.5%
7424
 
0.4%
8416
 
0.4%
15405
 
0.4%
Other values (25967)90922
94.9%
ValueCountFrequency (%)
0.581
 
< 0.1%
1495
0.5%
1.51
 
< 0.1%
1.622
 
< 0.1%
2562
0.6%
2.081
 
< 0.1%
2.52
 
< 0.1%
2.571
 
< 0.1%
2.781
 
< 0.1%
2.91
 
< 0.1%
ValueCountFrequency (%)
7263001
< 0.1%
7129001
< 0.1%
7113001
< 0.1%
6999001
< 0.1%
6875001
< 0.1%
6869001
< 0.1%
6721001
< 0.1%
6576001
< 0.1%
6412001
< 0.1%
6367001
< 0.1%

Production_in_tons
Real number (ℝ)

High correlation 

Distinct32495
Distinct (%)33.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37057.867
Minimum0.01
Maximum2589591
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-21T11:16:42.976086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile10
Q1179
median1575
Q314601.5
95-th percentile216134
Maximum2589591
Range2589591
Interquartile range (IQR)14422.5

Descriptive statistics

Standard deviation116817.9
Coefficient of variation (CV)3.1523104
Kurtosis70.734397
Mean37057.867
Median Absolute Deviation (MAD)1555
Skewness6.8213819
Sum3.5500696 × 109
Variance1.3646422 × 1010
MonotonicityNot monotonic
2025-11-21T11:16:43.090504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2546
 
0.6%
1538
 
0.6%
3491
 
0.5%
10455
 
0.5%
4438
 
0.5%
5422
 
0.4%
6417
 
0.4%
100383
 
0.4%
8378
 
0.4%
7363
 
0.4%
Other values (32485)91367
95.4%
ValueCountFrequency (%)
0.015
 
< 0.1%
0.133
< 0.1%
0.216
< 0.1%
0.315
< 0.1%
0.311
 
< 0.1%
0.381
 
< 0.1%
0.418
< 0.1%
0.520
< 0.1%
0.511
 
< 0.1%
0.551
 
< 0.1%
ValueCountFrequency (%)
25895911
< 0.1%
24652121
< 0.1%
24109631
< 0.1%
23908401
< 0.1%
23563891
< 0.1%
23500431
< 0.1%
23432571
< 0.1%
23376931
< 0.1%
20704971
< 0.1%
20479181
< 0.1%

Yield_ton_per_hec
Real number (ℝ)

High correlation 

Distinct71051
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6028547
Minimum0.00051413882
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-21T11:16:43.196753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.00051413882
5-th percentile0.20450372
Q10.59816525
median1.3278995
Q32.8978943
95-th percentile15.451731
Maximum93
Range92.999486
Interquartile range (IQR)2.2997291

Descriptive statistics

Standard deviation6.7294769
Coefficient of variation (CV)1.867818
Kurtosis27.743528
Mean3.6028547
Median Absolute Deviation (MAD)0.87386048
Skewness4.4412684
Sum345146.28
Variance45.285859
MonotonicityNot monotonic
2025-11-21T11:16:43.304142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1982
 
1.0%
0.5695
 
0.7%
2479
 
0.5%
0.3333333333349
 
0.4%
1.5297
 
0.3%
0.6666666667289
 
0.3%
0.6250
 
0.3%
3247
 
0.3%
0.4242
 
0.3%
0.25225
 
0.2%
Other values (71041)91743
95.8%
ValueCountFrequency (%)
0.00051413881751
< 0.1%
0.00081321691491
< 0.1%
0.001173192551
< 0.1%
0.0012277470841
< 0.1%
0.0012777326051
< 0.1%
0.0012820512821
< 0.1%
0.0013774104681
< 0.1%
0.0016849199661
< 0.1%
0.002439024391
< 0.1%
0.0031886972951
< 0.1%
ValueCountFrequency (%)
931
< 0.1%
912
< 0.1%
90.833333331
< 0.1%
90.826086961
< 0.1%
90.823529411
< 0.1%
90.815789471
< 0.1%
90.814285711
< 0.1%
90.813432841
< 0.1%
90.81251
< 0.1%
90.807486631
< 0.1%

Interactions

2025-11-21T11:16:39.268112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:29.676960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:30.673964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:31.627745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:32.617873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:33.599424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:34.642837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:36.353311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:37.328016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:38.330356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:39.352915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:29.776597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:30.772601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:31.728177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:32.712605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:33.689768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:34.734600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:36.454135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:37.424372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:38.419391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:39.450077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:29.875167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:30.863451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:31.854773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:32.812315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:33.783623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:35.531416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:36.550452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:37.523457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:38.514414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:39.534292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:29.970803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:30.955667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:31.953477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:32.905158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:33.882197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:35.619677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:36.646631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:37.616218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:38.602852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:39.624115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:30.080126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:31.056518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:32.053832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:33.004120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:33.987203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:35.717766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:36.742375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:37.717821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:38.697450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:39.713025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:30.173878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:31.152411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:32.150943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:33.102869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:34.098047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:35.822594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:36.836781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:37.817400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:38.792147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:39.804884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:30.268069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:31.249097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:32.242794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:33.200001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:34.219024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:35.927555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:36.930243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:37.917488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:38.889215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:39.895340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:30.367715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:31.343951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:32.334175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:33.306303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:34.351728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:36.039068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:37.029688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:38.017760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:38.982885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:39.998006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:30.466145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:31.439656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:32.431547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:33.406915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:34.453746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:36.151196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:37.135014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:38.123661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:39.088194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:40.089182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:30.560926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:31.532129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:32.522073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:33.503383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:34.555974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:36.255214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:37.239300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:38.231962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-21T11:16:39.181269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-21T11:16:43.392777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Area_in_hectaresCropCrop_TypeKNPProduction_in_tonsState_NameUnnamed: 0Yield_ton_per_hecpHrainfalltemperature
Area_in_hectares1.0000.1420.105-0.1310.069-0.0910.8980.093-0.037-0.0050.050-0.139-0.045
Crop0.1421.0000.6331.0001.0001.0000.1240.1520.0870.3330.6890.3070.243
Crop_Type0.1050.6331.0000.4360.4050.3990.0640.2770.0670.2060.3280.5880.451
K-0.1311.0000.4361.0000.5270.2140.1200.1500.0050.555-0.1090.258-0.053
N0.0691.0000.4050.5271.0000.2610.3360.1570.0070.631-0.1560.1110.030
P-0.0911.0000.3990.2140.2611.0000.0630.1570.0040.268-0.2450.139-0.031
Production_in_tons0.8980.1240.0640.1200.3360.0631.0000.125-0.0170.408-0.007-0.088-0.062
State_Name0.0930.1520.2770.1500.1570.1570.1251.0000.1220.1140.0910.6360.560
Unnamed: 0-0.0370.0870.0670.0050.0070.004-0.0170.1221.0000.045-0.000-0.044-0.033
Yield_ton_per_hec-0.0050.3330.2060.5550.6310.2680.4080.1140.0451.000-0.1390.058-0.060
pH0.0500.6890.328-0.109-0.156-0.245-0.0070.091-0.000-0.1391.000-0.0160.030
rainfall-0.1390.3070.5880.2580.1110.139-0.0880.636-0.0440.058-0.0161.0000.156
temperature-0.0450.2430.451-0.0530.030-0.031-0.0620.560-0.033-0.0600.0300.1561.000

Missing values

2025-11-21T11:16:40.223504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-21T11:16:40.374377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0State_NameCrop_TypeCropNPKpHrainfalltemperatureArea_in_hectaresProduction_in_tonsYield_ton_per_hec
00andhra pradeshkharifcotton12040205.46654.3429.2666677300.09400.01.287671
11andhra pradeshkharifhorsegram2060206.18654.3429.2666673300.01000.00.303030
22andhra pradeshkharifjowar8040405.42654.3429.26666710100.010200.01.009901
33andhra pradeshkharifmaize8040205.62654.3429.2666672800.04900.01.750000
44andhra pradeshkharifmoong2040205.68654.3429.2666671300.0500.00.384615
55andhra pradeshkharifragi5040205.64654.3429.2666676700.011800.01.761194
66andhra pradeshkharifrice8040405.54654.3429.26666735600.075400.02.117978
77andhra pradeshkharifsunflower5060305.36654.3429.26666735900.011100.00.309192
88andhra pradeshrabihorsegram2060206.00288.3025.460000600.0200.00.333333
99andhra pradeshrabijowar8040405.50288.3025.46000018800.09400.00.500000
Unnamed: 0State_NameCrop_TypeCropNPKpHrainfalltemperatureArea_in_hectaresProduction_in_tonsYield_ton_per_hec
9983999839west bengalkharifmoong2040205.501166.9428.333333293.0136.00.464164
9984099840west bengalkharifsunflower5060305.621166.9428.33333337.040.01.081081
9984199841west bengalrabimoong2040205.62152.5422.28000052.042.00.807692
9984299842west bengalrabipotato18060904.84152.5422.280000977.015920.016.294780
9984399843west bengalrabirapeseed5040205.12152.5422.280000886.0542.00.611738
9984499844west bengalrabiwheat6030306.70152.5422.2800002013.05152.02.559364
9984599845west bengalsummermaize8040205.68182.5029.200000258.0391.01.515504
9984699846west bengalsummerrice8040405.64182.5029.200000105.0281.02.676190
9984799847west bengalrabirice8040405.42152.5422.280000152676.0261435.01.712352
9984899848west bengalrabisesamum3015306.54152.5422.280000244.095.00.389344